Cotinine concentrations in maternal serum and amniotic fluid during pregnancy and risk of testicular germ cell cancer in the offspring: A prospective nested case-control study.

Maternal smoking in pregnancy may increase the risk of testicular germ cell cancer (TGCC) in offspring, but current evidence remains inconclusive. We performed a nested case-control study using cotinine measurements in maternal serum and amniotic fluid as a biomarker for tobacco exposure during pregnancy. A total of 654 males with maternal serum (n = 359, ncases/controls = 71/288) and/or amniotic fluid (n = 295, ncases/controls = 66/229) samples were included. Data on TGCC diagnoses and relevant covariates were derived from nationwide Danish health registries. Cotinine was quantified by liquid chromatography tandem mass spectrometry. An adapted cox regression model estimated the risk of TGCC considering active and inactive tobacco use defined according to cotinine concentrations of <, ≥15 ng/ml. Overall, the concentrations of cotinine were comparable in maternal serum and amniotic fluid (medianserum/amniotic fluid : 2.1/2.6 ng/ml). A strong statistically significant correlation was detected in 14 paired samples (Spearman rho: 0.85). Based on maternal serum cotinine concentrations, exposure to active tobacco use was not associated with risk of TGCC in offspring (HR 0.88, 95% CI 0.51; 1.52). Similarly, based on amniotic fluid cotinine concentrations, exposure to active tobacco use was not associated with risk of TGCC (HR 1.11, 95% CI 0.64; 1.95). However, different risks were observed for seminomas and nonseminomas in both matrices, but none were statistically significant. Our findings did not provide convincing evidence supporting that exposure to tobacco during pregnancy is associated with TGCC.

Assessing household lifestyle exposures from consumer purchases, the My Purchases cohort.

Consumer purchase data (CPD) is a promising instrument to assess the impact of purchases on health, but is limited by the need for manual scanning, a lack of access to data from multiple retailers, and limited information on product data and health outcomes. Here we describe the My Purchases cohort, a web-app enabled, prospective collection of CPD, covering several large retail chains in Denmark, that enables linkage to health outcomes. The cohort included 459 participants as of July 03, 2023. Up to eight years of CPD have been collected, with 2,225,010 products purchased, comprising 223,440 unique products. We matched 88.5% of all products by product name or item number to one generic food database and three product databases. Combined, the databases enable analysis of key exposures such as nutrients, ingredients, or additives. We found that increasing the number of retailers that provide CPD for each consumer improved the stability of individual CPD profiles and when we compared kilojoule information from generic and specific product matches, we found a median modified relative difference of 0.23. Combined with extensive product databases and health outcomes, CPD could provide the basis for extensive investigations of how what we buy affects our health.

Epidemiological characterisation of the first 785 SARS-CoV-2 Omicron variant cases in Denmark, December 2021.

By 9 December 2021, 785 SARS-CoV-2 Omicron variant cases have been identified in Denmark. Most cases were fully (76%) or booster-vaccinated (7.1%); 34 (4.3%) had a previous SARS-CoV-2 infection. The majority of cases with available information reported symptoms (509/666; 76%) and most were infected in Denmark (588/644; 91%). One in five cases cannot be linked to previous cases, indicating widespread community transmission. Nine cases have been hospitalised, one required intensive care and no deaths have been registered.

Human exposome assessment platform.

The Human Exposome Assessment Platform (HEAP) is a research resource for the integrated and efficient management and analysis of human exposome data. The project will provide the complete workflow for obtaining exposome actionable knowledge from population-based cohorts. HEAP is a state-of-the-science service composed of computational resources from partner institutions, accessed through a software framework that provides the world's fastest Hadoop platform for data warehousing and applied artificial intelligence (AI). The software, will provide a decision support system for researchers and policymakers. All the data managed and processed by HEAP, together with the analysis pipelines, will be available for future research. In addition, the platform enables adding new data and analysis pipelines. HEAP's final product can be deployed in multiple instances to create a network of shareable and reusable knowledge on the impact of exposures on public health.

Clustering cliques for graph-based summarization of the biomedical research literature.

BACKGROUND: Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts). RESULTS: SemRep is used to extract semantic predications from the citations returned by a PubMed search. Cliques were identified from frequently occurring predications with highly connected arguments filtered by degree centrality. Themes contained in the summary were identified with a hierarchical clustering algorithm based on common arguments shared among cliques. The validity of the clusters in the summaries produced was compared to the Silhouette-generated baseline for cohesion, separation and overall validity. The theme labels were also compared to a reference standard produced with major MeSH headings. CONCLUSIONS: For 11 topics in the testing data set, the overall validity of clusters from the system summary was 10% better than the baseline (43% versus 33%). While compared to the reference standard from MeSH headings, the results for recall, precision and F-score were 0.64, 0.65, and 0.65 respectively.

Graph-based methods for discovery browsing with semantic predications.

We present an extension to literature-based discovery that goes beyond making discoveries to a principled way of navigating through selected aspects of some biomedical domain. The method is a type of "discovery browsing" that guides the user through the research literature on a specified phenomenon. Poorly understood relationships may be explored through novel points of view, and potentially interesting relationships need not be known ahead of time. In a process of "cooperative reciprocity" the user iteratively focuses system output, thus controlling the large number of relationships often generated in literature-based discovery systems. The underlying technology exploits SemRep semantic predications represented as a graph of interconnected nodes (predication arguments) and edges (predicates). The system suggests paths in this graph, which represent chains of relationships. The methodology is illustrated with depressive disorder and focuses on the interaction of inflammation, circadian phenomena, and the neurotransmitter norepinephrine. Insight provided may contribute to enhanced understanding of the pathophysiology, treatment, and prevention of this disorder.

A bottom-up approach to MEDLINE indexing recommendations.

MEDLINE indexing performed by the US National Library of Medicine staff describes the essence of a biomedical publication in about 14 Medical Subject Headings (MeSH). Since 2002, this task is assisted by the Medical Text Indexer (MTI) program. We present a bottom-up approach to MEDLINE indexing in which the abstract is searched for indicators for a specific MeSH recommendation in a two-step process. Supervised machine learning combined with triage rules improves sensitivity of recommendations while keeping the number of recommended terms relatively small. Improvement in recommendations observed in this work warrants further exploration of this approach to MTI recommendations on a larger set of MeSH headings.

Knowledge discovery in neuroinformatics.

Traditionally, the process of turning data into biomedical knowledge has involved "manual" meta-analyses of results reported in journals. Since the amount of scientific data produced in neuroscience today increases dramatically, the resultant expansion of the medical databases has created a significant potential for the design of new data modeling and information retrieval tools and services that enable faster data processing, analysis and dissemination among a highly interdisciplinary community of researchers.